We reformulate the statement that the theory of the free group is stable in terms of simplicial diagram chasing and profinite sets, without any terminology from logic. This includes three characterisations of stability (via indiscernible sequences, counting types, and definable types), and the notions of a first order theory and a model.

We do so by generalising slightly and allowing the universe of a first order structure/model to be an arbitrary (symmetric) simplicial set: formulas and basic predicates now may denote sets of simplices of an arbitrary (symmetric) simplicial set rather than sets of tuples of elements of a set. In this generalised sense the type space functor of a theory is its universal model classifying its usual models: taking the type of a tuple gives a map from a usual model of a theory to its type space functor. We define a property of simplicial maps weaker then being a fibration, and find it appears in the conditions characterising which maps correspond to models, when the generalised semantics is well-behaved, and which symmetric simplicial profinite sets correspond to first order theories.

This talk will be an introduction to quivers and their representation theory.

Jing-Yuan Wang is going to talk about: 'A Runtime-Data-Driven Enhancement Preconditioner for PCG for a Sequence of SPD Linear Systems'
 

In this work, we propose a runtime-data-driven enhancement preconditioner for improving the convergence of a preconditioned conjugate gradient method for solving a sequence of symmetric positive definite linear systems of equations. The methodology is designed for the situation where a subset of the systems has been solved and the convergence is considered too slow. In such a situation, data generated from the solved problems (residual vectors, intermediate solution vectors, approximate error vectors) are first analyzed by an unsupervised learning algorithm as a 3-step process: (1) dimension reduction; (2) classification of the slow features; (3) construction of projections to each of the feature subspaces. Based on the results of the analysis, one or more enhancement preconditioners are constructed using projection matrices corresponding to the features extracted from the slow convergence subspaces. The enhancement preconditioners are additively incorporated into the existing preconditioners and are employed to solve other systems in the sequence. The enhancement preconditioner can be further enhanced when necessary by repeating this process. Numerical experiments for time-dependent problems, including parabolic and hyperbolic equations, and stochastic elliptic equations demonstrate that the proposed approach improves the convergence considerably for other systems in the sequence when classical preconditioners are insufficient.